Predicting the probability that a user will click on a specific advertisement
has been a prevalent issue in online advertising, attracting much research
attention in the past decades. As a hot research frontier driven by industrial
needs, recent years have witnessed more and more novel learning models employed
to improve advertising CTR prediction. Although extant research provides
necessary details on algorithmic design for addressing a variety of specific
problems in advertising CTR prediction, the methodological evolution and
connections between modeling frameworks are precluded. However, to the best of
our knowledge, there are few comprehensive surveys on this topic. We make a
systematic literature review on state-of-the-art and latest CTR prediction
research, with a special focus on modeling frameworks. Specifically, we give a
classification of state-of-the-art CTR prediction models in the extant
literature, within which basic modeling frameworks and their extensions,
advantages and disadvantages, and performance assessment for CTR prediction are
presented. Moreover, we summarize CTR prediction models with respect to the
complexity and the order of feature interactions, and performance comparisons
on various datasets. Furthermore, we identify current research trends, main
challenges and potential future directions worthy of further explorations. This
review is expected to provide fundamental knowledge and efficient entry points
for IS and marketing scholars who want to engage in this area.

本文系统地回顾了 CTR 预测领域的最新研究进展及其建模框架，着重介绍了基础建模框架及其拓展，对其优缺点和预测性能进行了分类总结，并结合各类数据集对不同复杂度和要素相互作用顺序进行了性能比较。同时，本文还识别出当前研究趋势、主要挑战和未来研究方向，为有意从事该领域的学者提供了基础知识和高效的进入点。